A new British startup believes that artificial intelligence is the key to speedrunning the slow and temperamental process of drug design.

Potential Sciences plans to build large-scale chemical datasets by using basic principles of physics to simulate interactions between atoms, then small molecules, then complex amino acids and proteins. With that data, it’s betting it can train AI models and prediction systems capable of identifying highly targeted drugs for specific diseases, bypassing costly and time-consuming lab experimentation.

“We want molecules that are never going to work in the clinic to fail fast,” Hatem Helal, Potential Science’s founder and CEO, tells WIRED. “That means having better predictors of how they will behave as they interact with human biology.”

The startup, which launched on November 5, says it has seed funding in the tens of millions of dollars from SovAI—the UK government’s AI venture capital fund—and Twin Path Ventures, among other investors. The founders declined to disclose the precise figure or valuation.

If things pan out as Potential Sciences hopes, it could pave the way for the accelerated development of new treatments for all sorts of diseases. By reducing the cost of experimentation, Helal says, the company’s approach could also make it economical for pharmaceutical companies to pursue drugs for rare conditions that have previously gone untreated.

“We want to make it possible to have more bespoke treatments for diseases—this idea that medicines can be one-off molecular combinations that are tailored to your unique human biology,” he says.

The question is whether Potential Sciences will be able to predict how well drug candidates will bind to disease-related proteins as accurately as it hopes, which would require a marked leap forward.

Source link